2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning

Stephen T. Wong; Dongbing Gao; Jianting Sheng; Wenjuan Dong; Zheng Yin

doi:10.1158/1538-7445.AM2025-2487

2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning

Stephen T. Wong, Dongbing Gao, Jianting Sheng, Wenjuan Dong, Zheng Yin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Reconstructing cell division lineages across generations is central to understanding developmental biology, cancer progression, cell differentiation, drug resistance, and tissue aging. By integrating CRISPR-Cas9 induced gene editing with single cell RNA-Seq, high-resolution lineage tracing through molecular barcoding becomes feasible. However, tracking mutation evolution across successive guide RNA edits, especially with homing guide RNA (hgRNA) technology, poses challenges due to ongoing dynamic retargeting and genetic modifications that complicate lineage reconstruction.

Original language	English (US)
Title of host publication	LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning
Publisher	Cancer Research
Volume	85
Edition	8_Supplement_1: 2487
ISBN (Electronic)	1538-7445
ISBN (Print)	0008-5472
DOIs	https://doi.org/10.1158/1538-7445.AM2025-2487
State	Published - Apr 21 2025
Event	American Association for Cancer Research - Duration: Apr 25 2025 → Apr 30 2025

Conference

Conference	American Association for Cancer Research
Period	4/25/25 → 4/30/25

Divisions

Medical Oncology

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10.1158/1538-7445.AM2025-2487License: Unspecified

Cite this

Wong, S. T., Gao, D., Sheng, J., Dong, W., & Yin, Z. (2025). 2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. In LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning (8_Supplement_1: 2487 ed., Vol. 85). Cancer Research. https://doi.org/10.1158/1538-7445.AM2025-2487

2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. / Wong, Stephen T.; Gao, Dongbing; Sheng, Jianting et al.
LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. Vol. 85 8_Supplement_1: 2487. ed. Cancer Research, 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wong, ST, Gao, D, Sheng, J , Dong, W & Yin, Z 2025, 2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. in LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. 8_Supplement_1: 2487 edn, vol. 85, Cancer Research, American Association for Cancer Research, 4/25/25. https://doi.org/10.1158/1538-7445.AM2025-2487

Wong ST, Gao D, Sheng J , Dong W , Yin Z. 2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. In LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning. 8_Supplement_1: 2487 ed. Vol. 85. Cancer Research. 2025 doi: 10.1158/1538-7445.AM2025-2487

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abstract = "Reconstructing cell division lineages across generations is central to understanding developmental biology, cancer progression, cell differentiation, drug resistance, and tissue aging. By integrating CRISPR-Cas9 induced gene editing with single cell RNA-Seq, high-resolution lineage tracing through molecular barcoding becomes feasible. However, tracking mutation evolution across successive guide RNA edits, especially with homing guide RNA (hgRNA) technology, poses challenges due to ongoing dynamic retargeting and genetic modifications that complicate lineage reconstruction.",

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AU - Yin, Zheng

PY - 2025/4/21

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AB - Reconstructing cell division lineages across generations is central to understanding developmental biology, cancer progression, cell differentiation, drug resistance, and tissue aging. By integrating CRISPR-Cas9 induced gene editing with single cell RNA-Seq, high-resolution lineage tracing through molecular barcoding becomes feasible. However, tracking mutation evolution across successive guide RNA edits, especially with homing guide RNA (hgRNA) technology, poses challenges due to ongoing dynamic retargeting and genetic modifications that complicate lineage reconstruction.

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DO - 10.1158/1538-7445.AM2025-2487

M3 - Conference contribution

SN - 0008-5472

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BT - LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning

PB - Cancer Research

T2 - American Association for Cancer Research

Y2 - 25 April 2025 through 30 April 2025

ER -

2487: LINMAP: A framework for comprehensive cell division lineage reconstruction via molecular barcoding and machine learning

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